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AOSM2022: Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study
Section 1: Publication
Authorship or Presenters
Yusof Ghiasi, Claude R. Duguay, Justin Murfitt, Wu Yuhao, Milad Asgarimehr
Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study
Hydrometeorology, Atmosphere and Extremes
Yusof Ghiasi, Claude R. Duguay, Justin Murfitt, Wu Yuhao, Milad Asgarimehr (2022). Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study. Proceedings of the GWF Annual Open Science Meeting, May 16-18, 2022.
Section 2: Abstract
Plain Language Summary
There has been a rapidly growing interest in the use of Global Navigation Satellite System Reflectometry (GNSS-R) to monitor a variety of geophysical parameters over the last two decades. However, within cryosphere and hydrosphere studies, few have yet been dedicated to the retrieval of lake ice cover, which is an important physical feature playing a role in climate and affecting the economy and livelihood of northern regions. In this paper, GNSS-R technique is employed for assessing seasonal timing of annual ice cover (lake ice phenology) for Qinghai Lake, Tibet Plateau. To this aim, the Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation from August 2018 to March 2022 (V. 3.0) were used to examine how reflected GNSS signals are modified by changing lake ice surface properties and the freezing/thawing states of the lake. A moving t-test (MTT) algorithm applied to SNR timeseries over three ice seasons allowed for detection of lake ice at daily temporal resolution. A strong agreement was found between ice phenology records derived from CYGNSS and those obtained from the visual interpretation of Moderate Resolution Imaging Spectroradiometer (MODIS) color composite images. Over the three years of observations, the error for CYGNSS freeze-up timing ranged from 0 to 8 days with an average of 2.5 days. However, the error for breakup timing ranged from 2 to 8 days with an average of 2.5 days and showed the sensitivity of CYGNSS to the onset of spring melt before moving into open water conditions. Moreover, all seven t-score spikes appeared within the freeze-up and breakup periods visually obtained from MODIS images. In addition, results showed a drop in SNR values in the presence of ice cover compared to those from open water. We find that this incongruity with previous GNSS-R studies over sea ice, which have shown a higher reflection power from the sea ice surface, is due to differences in salinity and roughness of frozen lakes and oceans.
Section 3: Miscellany
University of Waterloo
First Author: Yusof Ghiasi
Additional Authors: Claude R. Duguay; Justin Murfitt, Wu Yuhao, Milad Asgarimehr
Section 4: Download
T-2022-04-24-Z1UDn4qu9n0C0LczbZ1BmySA Conference Publication 1.0